Linear Moments█ OVERVIEW
The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values.
█ CONCEPTS
█ Four moments of a distribution
We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution.
The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution.
One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters.
█ Order Statistics
L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity.
To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required.
█ Bias and efficiency of an estimator
One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters.
Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases.
The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters.
In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively.
Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population.
█ WAYS TO USE THIS INDICATOR
The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies.
One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution.
█ FEATURES
Source Settings
Source - Select the source you wish the indicator to calculate on
Source Selection - Selec whether you wish to calculate on the source value or its log return
Moments Settings
Moments Selection - Select the L-moment you wish to be displayed
Lookback - Determine the sample size you wish the L-moments to be calculated with
Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
Statistics
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel.
█ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values.
█ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical Volatility Rank, and Historical Volatility Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models.
Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias.
Pros:
• Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator.
Cons:
• The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.
• Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.
• Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator:
• Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.)
Garman-Klass Estimator:
• Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.
• The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility.
Rogers-Satchell Estimator:
• Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.
• The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.
• Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension:
• Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.
• The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator:
• The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.
• This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.
• 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV.
EWMA Estimator:
• EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.
• EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.
• RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.
• However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH.
Adjusted Mean Absolute Deviation Estimator:
• This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.
• It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.
• The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.
• In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility.
Adjusted Median Absolute Deviation Estimator:
• This is another estimator that does not use standard deviation to measure volatility.
• Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.
• The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
█ FEATURES
• You can select the volatility estimator models in the Volatility Model input
• Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.
• Alpha is used to adjust the Yang Zhang volatility estimator value.
• Percentile Length is used to Adjust Percentile coloring lookbacks.
• The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.
• When percentile coloring is off, it won’t show the gradient color.
• You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.
• When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
• You can choose the plot style, such as lines, columns, areas in the plotstyle input.
• When the show information panel is on, a small panel will display on the right.
• The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value.
█ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move).
█ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
SYMBOL NOTES - UNCORRELATED TRADING GROUPSWrite symbol-specific notes that only appear on that chart. Organized into 6 uncorrelated groups for safe multi-pair trading.
📝 SYMBOL NOTES - UNCORRELATED TRADING GROUPS
This indicator solves two problems every serious trader faces:
1. Keeping Track of Your Analysis
Write notes for each trading pair and they'll only appear when you view that specific chart. No more forgetting your key levels, trade ideas, or analysis!
2. Avoiding Correlated Risk
The symbols are organized into 6 groups where ALL pairs within each group are completely UNCORRELATED. Trade any combination from the same group without worrying about double exposure.
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🎯 THE PROBLEM THIS SOLVES
Have you ever:
- Opened XAUUSD and EURUSD at the same time, then Fed news hit and BOTH positions went against you?
- Traded GBPUSD and GBPJPY together, then BOE announcement stopped out both trades?
- Forgotten what levels you were watching on a pair?
This indicator helps you avoid these costly mistakes!
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📁 THE 6 UNCORRELATED GROUPS
Each group contains pairs that share NO common currency:
```
GRUP 1: XAUUSD • EURGBP • NZDJPY • AUDCHF • NATGAS
GRUP 2: EURUSD • GBPJPY • AUDNZD • CADCHF
GRUP 3: GBPUSD • EURJPY • AUDCAD • NZDCHF
GRUP 4: USDJPY • EURCHF • GBPAUD • NZDCAD
GRUP 5: USDCAD • EURAUD • GBPCHF
GRUP 6: NAS100 • DAX40 • UK100 • JPN225
```
**Example - GRUP 1:**
- XAUUSD → Uses USD + Gold
- EURGBP → Uses EUR + GBP
- NZDJPY → Uses NZD + JPY
- AUDCHF → Uses AUD + CHF
- NATGAS → Commodity (independent)
= 7 different currencies, ZERO overlap!
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**✅ HOW TO USE**
1. Add indicator to any chart
2. Open Settings (gear icon ⚙️)
3. Find your symbol's group and input field
4. Write your note (support levels, trade ideas, etc.)
5. Switch charts - your note appears only on that symbol!
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⚙️ SETTINGS
- Note Position: Choose where the note box appears (6 positions)
- Text Size: Tiny, Small, Normal, or Large
- Show Group Name: Display which correlation group
- Show Symbol Name: Display current symbol
- Colors: Customize background, text, group label, and border colors
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💡 TRADING STRATEGY TIPS
Safe Multi-Pair Trading:
1. Pick ONE group for the day
2. Look for setups on ANY symbol in that group
3. Open positions freely - they won't correlate!
4. Even if major news hits, only ONE position is affected
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🔧 COMPATIBLE WITH
- All major forex brokers
- Prop firms (FTMO, Alpha Capital, etc.)
- Works on any timeframe
- Futures symbols supported (MGC, M6E, etc.)
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Visible RangeOverview This is a precision tool designed for quantitative traders and engineers who need exact control over their chart's visual scope. Unlike standard time calculations that fail in markets with trading breaks (like A-Shares, Futures, or Stocks), this indicator uses a loop-back mechanism to count the actual number of visible bars, ensuring your indicators (e.g., MA60, MA200) have sufficient sample data.
Why use this? If you use multi-timeframe layouts (e.g., Daily/Hourly/15s), it is critical to know exactly how much data is visible.
The Problem: In markets like the Chinese A-Share market (T+1, 4-hour trading day), calculating Time Range / Timeframe results in massive errors because it includes closed market hours (lunch breaks, nights, weekends).
The Solution: This script iterates through the visible range to count the true bar_index, providing 100% accurate data density metrics.
Key Features
True Bar Counting: Uses a for loop to count actual candles, ignoring market breaks. perfect for non-24/7 markets.
Integer Precision: Displays time ranges (Days, Hours, Mins, Secs) in clean integers. No messy decimals.
Compact UI: Displays information in a single line (e.g., View: 30 Days (120 Bars)), default to the Top Right corner to save screen space.
Fully Customizable: Adjustable position, text size, and colors to fit any dark/light theme.
Performance Optimized: Includes max_bars_back limits to prevent browser lag on deep history lookups.
Settings
Position: Default Top Right (can be moved to any corner).
Max Bar Count: Default 5000 (Safety limit for loop calculation).
Blockchain Fundamentals: PPT [CR]Blockchain Fundamentals: PPT
A proprietary market positioning indicator that analyzes price behavior using percentile-based statistical methods. The PPT (Percentile Position Transform) provides a normalized oscillator view of market conditions, helping traders identify potential trend exhaustion and reversal zones through multi-timeframe statistical analysis.
█ FEATURES
Dual Signal Lines
The indicator plots two distinct signals:
- White Line — Primary signal representing the normalized, smoothed market position. This is the main signal used for trading decisions.
- Red Line — Raw statistical measurement before final normalization. Useful for identifying divergences and signal development.
Background Coloring
Dynamic background colors provide at-a-glance market context:
- Green Background — Indicates bullish positioning when the primary signal exceeds the buffer threshold.
- Red Background — Indicates bearish positioning when the primary signal falls below the buffer threshold.
- Gray Background — Neutral zone where no clear directional bias is present.
Flip Buffer
An adjustable threshold system designed to reduce noise and false signals:
- Enable Flip Buffer — Toggle the buffer system on or off.
- Buffer Size — Adjustable threshold level (default -0.1) that determines when background colors change. Higher values reduce sensitivity; lower values increase responsiveness.
Reference Levels
Three horizontal reference lines provide context:
- Center line at 0 — Neutral market position.
- Upper dashed line at +1 — Extreme bullish positioning threshold.
- Lower dashed line at -1 — Extreme bearish positioning threshold.
█ HOW TO USE
Signal Interpretation
The indicator operates as a mean-reversion oscillator within a normalized range:
1 — Values approaching +1 suggest extended bullish conditions where price may be overextended relative to recent history.
2 — Values approaching -1 suggest extended bearish conditions where price may be oversold relative to recent history.
3 — Crosses of the center line (0) indicate shifts in the underlying statistical trend.
Trading Applications
While specific trading strategies will vary by individual approach and market conditions:
- Consider the extremes (+1 and -1 levels) as potential areas of interest for mean-reversion setups.
- Background color changes can help identify when market positioning shifts from one regime to another.
- Divergences between the white and red lines may provide early warning of potential trend changes.
- The buffer zone (gray background) represents areas where market positioning is relatively neutral.
█ LIMITATIONS
- The indicator requires sufficient historical data to function properly. In assets with limited price history, the statistical measurements may be less reliable during early data periods.
- As a percentile-based system, the indicator is relative to recent history. Changing market regimes may require interpretation adjustments.
- Not designed for high-frequency or scalping strategies due to its daily data dependency.
- Background colors are visual aids and should not be used as standalone trading signals without additional confirmation.
█ NOTES
This indicator is part of the Blockchain Fundamentals suite and represents proprietary research into statistical market positioning analysis.
Users should experiment with the buffer settings to match their risk tolerance and trading style. More conservative traders may prefer larger buffer values to reduce signal frequency, while active traders might benefit from smaller buffers that provide earlier warnings.
"Smart Dashboard" for Institutional Price Targets.This script is designed to create a "Smart Dashboard" for Institutional Price Targets.
Think of it as a tool that asks, "What does Wall Street think this stock is worth?" and then draws specific "Buy Zones" on your chart based on those professional valuations.
Here is a breakdown of how it works in plain English for an investor:
1. The Core Concept: Wall Street Consensus
The indicator doesn't use standard technical analysis (like RSI or Moving Averages). Instead, it looks at Fundamental Data. It pulls the average Price Target set by institutional analysts (banks, hedge funds, research firms).
Example: If Goldman Sachs, Morgan Stanley, and JP Morgan all agree that NVDA is worth $150, this tool grabs that $150 number.
2. The "Data Engine" (The Smart Part)
The code includes a sophisticated "search engine" (Section 2 & 3 of the code) to ensure it finds the most accurate price target.
The Problem: Sometimes data feeds are empty, or they are in the wrong currency (e.g., a Canadian stock showing a price target in USD, which makes the chart look broken).
The Solution: This script follows a "Waterfall" priority list to find data:
Priority 1: It checks NASDAQ data first (often the most accurate for tech stocks like Apple or Tesla).
Priority 2: If the local currency data is missing, it forces a search for USD data (this is the "USD Fix" in the title).
Priority 3: It checks NYSE data.
Backup: If all else fails, it uses the generic TradingView average.
In short: It works very hard to make sure it doesn't give you a blank screen or a currency error.
3. The "Institutional Buy Zones" (The Strategy)
Once the tool finds the "Fair Value" (the Analyst Target), it calculates deep discount levels where an institutional investor might want to buy the dip.
It draws four colored lines below the current price:
Target (Dashed Line): This is the Fair Value. (The goal).
Level 1 (Green Line - 90%): This is 10% below fair value. A standard "buy the dip" zone.
Level 2 (Blue Line - 70%): This is 30% below fair value. This is considered a "Value Buy" or a "Deep Discount."
Level 3 (Orange Line - ~66.5%): A specific Fibonacci-style extension of the deep discount.
Level 4 (Red Line - 63%): The "Crash" buy zone. If price hits this, the stock is trading massively below what analysts think it is worth.
4. The Dashboard
On the screen (top right by default), there is a clean table that summarizes everything:
Target: Tells you the exact price analysts are aiming for.
Dist %: Tells you how far away the current price is from that target (e.g., "+20%" means the stock needs to rise 20% to hit the target).
Source: Tells you where it found the data (e.g., "Nasdaq FQ"), so you know if the data is trustworthy.
How an Investor Uses This:
Validation: You want to buy a stock, but you check this tool. If the price is above the dashed Target line, the tool is telling you the stock is effectively "overpriced" compared to Wall Street's expectations.
Entry Points: You are waiting to enter a position. You set limit orders at the Green (90%) or Blue (70%) lines, knowing these are math-based discount levels relative to the company's fundamental valuation.
Summary: It automates the research process of looking up analyst price targets and draws "Sale Price" lines on your chart automatically.
🎯 SHORT BAG DETECTOR🎯 SHORT BAG DETECTOR: The Liquidation Squeeze Signal
💡 What This Indicator Does
The SHORT BAG DETECTOR is a powerful volatility and volume-based indicator designed to identify high-probability price areas where trapped short sellers (those holding a "short bag" of losing positions) are most vulnerable to a short squeeze or liquidation event.
It automatically scans for a rare confluence of three critical market conditions, generating a single, high-conviction signal (the large orange marker) for optimal entry timing.
🔎 The 3 Confluence Conditions
The main OLD BAG DETECTED! signal only triggers when all three of the following conditions occur simultaneously:
Old Level Touch: The price returns to a significant, aged historical pivot high or low price (established over the last 150 days). This level represents the average entry price for a large number of short or long positions.
Significant Gap: The current day opens with a meaningful price gap (user-defined percentage) against the direction of the trapped traders. This creates immediate urgency and stress for the "bag holders."
Volume Spike: The signal is confirmed by a massive volume spike (user-defined multiplier over average volume). This confirms that the movement is driven by forced liquidation (short-covering) and aggressive buying/selling, not just minor market noise.
📊 Key Features
High-Conviction Orange Signal: Marks the optimal timing for a potential squeeze/reversal driven by short liquidation.
Gap Markers (Green/Red): Clearly identifies significant bullish and bearish gaps on the chart.
Toggleable Minor Levels (Blue Labels): Shows all historical pivot levels being tracked for full context (can be easily disabled in the settings to reduce chart clutter).
📈 How to Use the Signal
The indicator is best used to identify continuation trades or volatile reversals. When the OLD BAG DETECTED! signal appears:
Bullish Signal (When price gaps up to an old low): Indicates a strong potential reversal as shorts from that low level are forced to cover.
Bearish Signal (When price gaps down to an old high): Indicates a potential reversal as longs from that high level are forced to liquidate.
This tool is perfect for traders looking to capitalize on volatility events and forced liquidations.
ES-VIX Expected Daily MoveThis indicator calculates the expected daily price movement for ES futures based on current volatility levels as measured by the VIX (CBOE Volatility Index).
Formula:
Expected Daily Move = (ES Price × VIX Price) / √252 / 100
The calculation converts the annualized VIX volatility into an expected daily move by dividing by the square root of 252 (the approximate number of trading days per year).
Features:
Real-time calculation using current ES futures price and VIX level
Histogram visualization in a separate pane for easy trend analysis
Information table displaying:
Current ES futures price
Current VIX level
Expected daily move in points
Expected daily move as a percentage
RSI os/ob overlay on candle - RichFintech.comRSI os/ob overlay on candle - RichFintech.com reduce the time your eyes must to look two pane, easier to analysis and tired eyes
ETH Upgrades: Exact Price + DateThis indicator places markers on the chart that show you the exact date and price where each Ethereum upgrade occurred.
Bitcoin vs. S&P 500 Performance Comparison**Full Description:**
**Overview**
This indicator provides an intuitive visual comparison of Bitcoin's performance versus the S&P 500 by shading the chart background based on relative strength over a rolling lookback period.
**How It Works**
- Calculates percentage returns for both Bitcoin and the S&P 500 (ES1! futures) over a specified lookback period (default: 75 bars)
- Compares the returns and shades the background accordingly:
- **Green/Teal Background**: Bitcoin is outperforming the S&P 500
- **Red/Maroon Background**: S&P 500 is outperforming Bitcoin
- Displays a real-time performance difference label showing the exact percentage spread
**Key Features**
✓ Rolling performance comparison using customizable lookback period (default 75 bars)
✓ Clean visual representation with adjustable transparency
✓ Works on any timeframe (optimized for daily charts)
✓ Real-time performance differential display
✓ Uses ES1! (E-mini S&P 500 continuous futures) for accurate comparison
✓ Fine-tuning adjustment factor for precise calibration
**Use Cases**
- Identify market regimes where Bitcoin outperforms or underperforms traditional equities
- Spot trend changes in relative performance
- Assess risk-on vs risk-off periods
- Compare Bitcoin's momentum against broader market conditions
- Time entries/exits based on relative strength shifts
**Settings**
- **S&P 500 Symbol**: Default ES1! (can be changed to SPX or other indices)
- **Lookback Period**: Number of bars for performance calculation (default: 75)
- **Adjustment Factor**: Fine-tune calibration to match specific data feeds
- **Transparency Controls**: Customize background shading intensity
- **Show/Hide Label**: Toggle performance difference display
**Best Practices**
- Use on daily timeframe for swing trading and position analysis
- Combine with other momentum indicators for confirmation
- Watch for color transitions as potential regime change signals
- Consider using multiple timeframes for comprehensive analysis
**Technical Details**
The indicator calculates rolling percentage returns using the formula: ((Current Price / Price ) - 1) × 100, then compares Bitcoin's return to the S&P 500's return over the same period. The background color dynamically updates based on which asset is showing stronger performance.
TheGrowth Checklist// -----------------------------------------------------------------------------
// TheGrowth Checklist Indicator
// Authors: Prochyy & Filip Moskal (The Growth Elite Mentor) | © 2025
//
// This indicator is provided strictly for personal use.
// You are welcome to use it in your own trading if you find it valuable.
//
// However, you are NOT allowed to:
// – copy or redistribute this script,
// – sell, publish, or otherwise commercialize it,
// – modify and distribute altered versions,
// – claim this work as your own.
//
// This tool was created specifically for the trading strategy used within
// The Growth Elite community:
// filip-moskal.mykajabi.com
//
// Please respect the creators' work. Thank you.
// -----------------------------------------------------------------------------
SIDD Table Volume multiframe (Modified)🚀 SIDD Volume Table – The Most Powerful Multi-Timeframe Volume Dashboard
Designed by Siddhartha Mukherjee (SIDD)
Free for the community.
Get an unfair edge with the cleanest, fastest, and most accurate multi-timeframe volume analyzer available on TradingView. This tool reveals where buyers and sellers are truly active across multiple timeframes—helping you confirm trends, avoid traps, and enter with confidence.
🔥 Why Traders Love This Indicator
✅ 1. Multi-Timeframe Volume Domination
Instantly view Buy% / Sell% / Total Volume for:
1m • 5m • 15m • 1H • 4H • 1D • 1W
Choose any combination you want!
✅ 2. Advanced Buy/Sell Volume Logic
Not simple volume…
This tool breaks it into:
Buy Volume% (green dominance)
Sell Volume% (red dominance)
Using candle structure (H-L-C), giving far more accurate pressure detection.
✅ 3. Realtime Candle Countdown
Never guess when a candle will close again.
Get:
Seconds (1m)
MM:SS (5m/15m/1H)
DD:HH:MM:SS (4H, 1D, 1W)
Perfect for scalpers, swing traders, and index traders.
✅ 4. Beautiful & Customizable Dashboard
Choose position anywhere on screen
Auto size or choose Tiny → Huge
Color-coded Bias (Green Buyers, Red Sellers)
Clean layout built for modern charts
Your chart stays clean while your data stays powerful.
💡 What This Helps You Identify
Where buyers are gaining strength
Where sellers are dominating
Multi-timeframe alignment (the key to big moves)
Real reversal pressure
Volume divergence across timeframes
Trend confirmation before breakouts
Perfect for:
NIFTY / BANKNIFTY / Stocks / Crypto / FX / Commodities
🧠 Who Should Use This?
Intraday traders
Swing traders
Options traders
Futures traders
Crypto scalpers
Professional volume analysts
If volume matters to you → this indicator becomes a must-have.
🛠 Built with Precision
Non-repainting
Multi-TF aligned
Fast + lightweight arrays
Uses BTC/ETH feed to stabilize ticks
Zero chart clutter
❤️ Free for Everyone
This tool is released 100% free to help the community trade with clarity and confidence.
Leave a like ⭐, comment 💬, or follow if you want more such institutional-grade tools.
⚠️ Disclaimer
This is for educational/analytical use only.
Not financial advice. Trade at your own risk.
Volatility Meter & Entry LineIndicator Name: Volatility Meter & Entry Line
Created by: Texas Trading Strategies
Overview
The "Volatility Meter & Entry Line" is a comprehensive, multi-factor technical analysis tool designed to help traders assess current market conditions and identify potential trading opportunities. It synthesizes three key market dimensions—momentum (RSI), market noise (Choppiness Index), and volatility (ATR)—into a single, easy-to-understand composite score. This score visually informs you whether the market is in a favorable state for trading or if it's better to avoid choppy, low-opportunity environments. Additionally, it plots a dynamic support/resistance line based on recent price wicks to aid in entry and exit planning.
⚠️ IMPORTANT: FINANCIAL RISK & LEGAL DISCLAIMER
PLEASE READ THIS CAREFULLY BEFORE USING THIS INDICATOR.
1. No Financial Advice: I am NOT a licensed financial advisor, broker, or certified financial planner. The indicator I have created and any accompanying descriptions are provided for EDUCATIONAL AND INFORMATIONAL PURPOSES ONLY. This is NOT financial advice. You should not construe any information provided here as a recommendation to buy, sell, or hold any financial instrument or asset class.
2. High Risk of Loss: Trading in financial markets (including stocks, forex, cryptocurrencies, futures, and CFDs) carries a HIGH LEVEL OF RISK and may not be suitable for all investors. There is a possibility you could sustain a loss of some, all, or in some cases (e.g., leveraged products), more than your initial investment. You should be aware of all the risks associated with trading and seek advice from an independent, qualified financial advisor if you have any doubts.
3. No Guarantee of Profit or Accuracy: Past performance is NOT indicative of future results. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed. The signals and metrics generated by this indicator are based on historical data and mathematical formulas. They are NOT guarantees of future market behavior and are inherently lagging. The indicator can and will produce losing signals.
4. Your Responsibility: You are solely responsible for your own trading decisions and for evaluating the merits and risks associated with the use of any information from this indicator. It is your responsibility to backtest and forward-test any strategy, understand its limitations, and only trade with capital you can afford to lose.
By using this indicator, you acknowledge that you have read, understood, and agree to this disclaimer and accept full responsibility for your own trading actions.
Detailed Indicator Description & Components
1. The Core Components (Inputs & Calculations)
RSI (Relative Strength Index): Measures the speed and change of price movements. It identifies overbought (typically above 70) and oversold (typically below 30) conditions. Your indicator allows you to adjust these thresholds.
Choppiness Index (CI): A volatility indicator designed to determine if a market is trending (low CI values) or ranging/choppy (high CI values). A value below 38.2 often suggests a trend, while a value above 61.8 suggests a choppy market. Your Choppy Market Threshold input allows for customization.
ATR-based Volatility Score: The Average True Range (ATR) is normalized as a percentage of the current price (atrPercent). This value is then compared to your High Volatility Threshold to create a VolatilityScore from 0 to 100. Higher scores indicate more volatility, which can be favorable for certain trading strategies.
2. The Composite Trading Signal (The "Meter")
This is the heart of the indicator. It combines the three components above into a single tradeScore (0-100) and categorizes the market condition.
GOOD TO TRADE (Lime Color): Triggered when tradeScore >= 70.
What it means: The market is likely exhibiting a favorable combination of high volatility (opportunity), extreme RSI readings (potential momentum exhaustion for reversals or breakouts), and low choppiness (a trending or clean-moving market).
MODERATE (Yellow Color): Triggered when 40 <= tradeScore < 70.
What it means: Market conditions are mixed. There may be some opportunity, but it's not as clear. This could be a period of consolidation or a weakening trend. Caution is advised.
CHOPPY / AVOID (Red Color): Triggered when tradeScore < 40.
What it means: The market is likely in a low-volatility, highly choppy, or directionless state. Trading in these conditions often leads to whipsaws and small, frustrating losses. The indicator suggests it's best to avoid entering new positions or to be extremely selective.
3. The Wick Line (For Entries & Exits)
What it is: A dynamic line that connects recent swing highs (the tops of candle wicks), effectively acting as a moving resistance line.
How to use it:
In an uptrend, a break above this line can confirm bullish strength.
In a downtrend or during a pullback, this line can act as resistance. A price rejection (e.g., a long wick touching the line) in a "GOOD TO TRADE" market could signal a short entry or a point to exit a long position.
The concept can be mirrored to plot a support line from swing lows (ta.pivotlow) for a more complete picture (this would require additional code).
How to Use This Indicator in Your Trading
Context First: Use the "Meter" for market context. Do not take trades when the meter is red ("CHOPPY/AVOID") unless you have a very high-conviction, proven strategy for such environments.
Signal Confirmation: Wait for the meter to turn green or yellow BEFORE looking for specific entry setups. This filters out low-quality market noise.
Entry Trigger: Use the "Wick Line" (resistance/support) or your own preferred entry method (e.g., candlestick patterns, break of structure) to time your entry, but only when the overall marketCondition is favorable.
Risk Management is Paramount: ALWAYS use a stop-loss. The indicator does not provide stop-loss levels. You must determine your risk management based on the ATR, the Wick Line, or support/resistance levels.
Remember: This indicator is a FILTER, not a crystal ball. Its purpose is to improve the odds of your trades by ensuring you are only trading when market conditions align with the strategy's logic. It should be one component of a complete trading plan that includes rigorous risk management.
Trend Zones This tool helps you quickly understand the market’s direction and the strength of the most recent price move:
It identifies whether the market is in an uptrend, downtrend, or flat/sideways phase and clearly marks these conditions on the chart.
It can notify you when the trend changes, so you don’t have to constantly watch the screen.
Each alert includes:
The current closing price
The previous closing price
The difference between the two closes (how much price has moved in one bar)
This makes it easier to see not only what the trend is, but also how strong the latest price move is when the alert triggers.
ATR Risk Manager v5.2 [Auto-Extrapolate]If you ever had problems knowing how much contracts to use for a particular timeframe to keep your risk within acceptable levels, then this indicator should help. You just have to define your accepted risk based on ATR and also percetage of your drawdown, then the indicator will tell you how many contracts you should use. If the risk is too high, it will also tell you not to trade. This is only for futures NQ MNQ ES MES GC MGC CL MCL MYM and M2K.
Omega Correlation [OmegaTools]Omega Correlation (Ω CRR) is a cross-asset analytics tool designed to quantify both the strength of the relationship between two instruments and the tendency of one to move ahead of the other. It is intended for traders who work with indices, futures, FX, commodities, equities and ETFs, and who require something more robust than a simple linear correlation line.
The indicator operates in two distinct modes, selected via the “Show” parameter: Correlation and Anticipation. In Correlation mode, the script focuses on how tightly the current chart and the chosen second asset move together. In Anticipation mode, it shifts to a lead–lag perspective and estimates whether the second asset tends to behave as a leader or a follower relative to the symbol on the chart.
In both modes, the core inputs are the chart symbol and a user-selected second symbol. Internally, both assets are transformed into normalized log-returns: the script computes logarithmic returns, removes short-term mean and scales by realized volatility, then clips extreme values. This normalisation allows the tool to compare behaviour across assets with different price levels and volatility profiles.
In Correlation mode, the indicator computes a composite correlation score that typically ranges between –1 and +1. Values near +1 indicate strong and persistent positive co-movement, values near zero indicate an unstable or weak link, and values near –1 indicate a stable anti-correlation regime. The composite score is constructed from three components.
The first component is a normalized return co-movement measure. After transforming both instruments into normalized returns, the script evaluates how similar those returns are bar by bar. When the two assets consistently deliver returns of similar sign and magnitude, this component is high and positive. When they frequently diverge or move in opposite directions, it becomes negative. This captures short-term co-movement in a volatility-adjusted way.
The second component focuses on high–low swing alignment. Rather than looking only at closes, it examines the direction of changes in highs and lows for each bar. If both instruments are printing higher highs and higher lows together, or lower highs and lower lows together, the swing structure is considered aligned. Persistent alignment contributes positively to the correlation score, while repeated mismatches between the swing directions reduce it. This helps differentiate between superficial price noise and structural similarity in trend behaviour.
The third component is a classical Pearson correlation on closing prices, computed over a longer lookback. This serves as a stabilising backbone that summarises general co-movement over a broader window. By combining normalized return co-movement, swing alignment and standard price correlation with calibrated weights, the Correlation mode provides a richer view than a single linear measure, capturing both short-term dynamic interaction and longer-term structural linkage.
In Anticipation mode, Omega Correlation estimates whether the second asset tends to lead or lag the current chart. The output is again a continuous score around the range. Positive values suggest that the second asset is acting more as a leader, with its past moves bearing informative value for subsequent moves of the chart symbol. Negative values indicate that the second asset behaves more like a laggard or follower. Values near zero suggest that no stable lead–lag structure can be identified.
The anticipation score is built from four elements inspired by quantitative lead–lag and price discovery analysis. The first element is a residual lead correlation, conceptually similar to Granger-style logic. The script first measures how much of the chart symbol’s normalized returns can be explained by its own lagged values. It then removes that component and studies the correlation between the residuals and lagged returns of the second asset. If the second asset’s past returns consistently explain what the chart symbol does beyond its own autoregressive behaviour, this residual correlation becomes significantly positive.
The second element is an asymmetric lead–lag structure measure. It compares the strength of relationships in both directions across multiple lags: the correlation of the current symbol with lagged versions of the second asset (candidate leader) versus the correlation of lagged values of the current symbol with the present values of the second asset. If the forward direction (second asset leading the first) is systematically stronger than the backward direction, the structure is skewed toward genuine leadership of the second asset.
The third element is a relative price discovery score, constructed by building a dynamic hedge ratio between the two prices and defining a spread. The indicator looks at how changes in each asset contribute to correcting deviations in this spread over time. When the chart symbol tends to do most of the adjustment while the second asset remains relatively stable, it suggests that the second asset is taking a greater role in determining the equilibrium price and the chart symbol is adjusting to it. The difference in adjustment intensity between the two instruments is summarised into a single score.
The fourth element is a breakout follow-through causality component. The script scans for breakout events on the second asset, where its price breaks out of a recent high or low range while the chart symbol has not yet done so. It then evaluates whether the chart symbol subsequently confirms the breakout direction, remains neutral, or moves against it. Events where the second asset breaks and the first asset later follows in the same direction add positive contribution, while failed or contrarian follow-through reduce this component. The contribution is also lightly modulated by the strength of the breakout, via the underlying normalized return.
The four elements of the Anticipation mode are combined into a single leading correlation score, providing a compact and interpretable measure of whether the second asset currently behaves as an effective early signal for the symbol you trade.
To aid interpretation, Omega Correlation builds dynamic bands around the active series (correlation or anticipation). It estimates a long-term central tendency and a typical deviation around it, plotting upper and lower bands that highlight unusually high or low values relative to recent history. These bands can be used to distinguish routine fluctuations from genuinely extreme regimes.
The script also computes percentile-based levels for the correlation series and uses them to track two special price levels on the main chart: lost correlation levels and gained correlation levels. When the correlation drops below an upper percentile threshold, the current price is stored as a lost correlation level and plotted as a horizontal line. When the correlation rises above a lower percentile threshold, the current price is stored as a gained correlation level. These levels mark zones where a historically strong relationship between the two markets broke down or re-emerged, and can be used to frame divergence, convergence and spread opportunities.
An information panel summarises, in real time, whether the second asset is behaving more as a leading, lagging or independent instrument according to the anticipation score, and suggests whether the current environment is more conducive to de-alignment, re-alignment or classic spread behaviour based on the correlation regime. This makes the tool directly interpretable even for users who are not familiar with all the underlying statistical details.
Typical applications for Omega Correlation include intermarket analysis (for example, index vs index, commodity vs related equity sector, FX vs bonds), dynamic hedge sizing, regime detection for algorithmic strategies, and the identification of lead–lag structures where a macro driver or benchmark can be monitored as an early signal for the instrument actually traded. The indicator can be applied across intraday and higher timeframes, with the understanding that the strength and nature of relationships will differ across horizons.
Omega Correlation is designed as an advanced analytical framework, not as a standalone trading system. Correlation and lead–lag relationships are statistical in nature and can change abruptly, especially around macro events, regime shifts or liquidity shocks. A positive anticipation reading does not guarantee that the second asset will always move first, and a high correlation regime can break without warning. All outputs of this tool should be combined with independent analysis, sound risk management and, when appropriate, backtesting or forward testing on the user’s specific instruments and timeframes.
The intention behind Omega Correlation is to bring techniques inspired by quantitative research, such as normalized return analysis, residual correlation, asymmetric lead–lag structure, price discovery logic and breakout event studies, into an accessible TradingView indicator. It is intended for traders who want a structured, professional way to understand how markets interact and to incorporate that information into their discretionary or systematic decision-making processes.
Roshan Dash Ultimate Trading DashboardHas the key moving averages sma (10,20,50,200) in daily and above timeframe. And for lower timeframe it has ema (10,20,50,200) and vwap. Displays key information like marketcap, sector, lod%, atr, atr% and distance of atr from 50sma . All things which help determine whether or not to take trade.
Multi-Ticker Anchored CandlesMulti-Ticker Anchored Candles (MTAC) is a simple tool for overlaying up to 3 tickers onto the same chart. This is achieved by interpreting each symbol's OHLC data as percentages, then plotting their candle points relative to the main chart's open. This allows for a simple comparison of tickers to track performance or locate relationships between them.
> Background
The concept of multi-ticker analysis is not new, this type of analysis can be extremely helpful to get a gauge of the over all market, and it's sentiment. By analyzing more than one ticker at a time, relationships can often be observed between tickers as time progresses.
While seeing multiple charts on top of each other sounds like a good idea...each ticker has its own price scale, with some being only cents while others are thousands of dollars.
Directly overlaying these charts is not possible without modification to their sources.
By using a fixed point in time (Period Open) and percentage performance relative to that point for each ticker, we are able to directly overlay symbols regardless of their price scale differences.
The entire process used to make this indicator can be summed up into 2 keywords, "Scaling & Anchoring".
> Scaling
First, we start by determining a frame of reference for our analysis. The indicator uses timeframe inputs to determine sessions which are used, by default this is set to 1 day.
With this in place, we then determine our point of reference for scaling. While this could be any point in time, the most sensible for our application is the daily (or session) open.
Each symbol shares time, therefore, we can take a price point from a specified time (Opening Price) and use it to sync our analysis over each period.
Over the day, we track the percentage performance of each ticker's OHLC values relative to its daily open (% change from open).
Since each ticker's data is now tracked based on its opening price, all data is now using the same scale.
The scale is simply "% change from open".
> Anchoring
Now that we have our scaled data, we need to put it onto the chart.
Since each point of data is relative to it's daily open (anchor point), relatively speaking, all daily opens are now equal to each other.
By adding the scaled ticker data to the main chart's daily open, each of our resulting series will be properly scaled to the main chart's data based on percentages.
Congratulations, We have now accurately scaled multiple tickers onto one chart.
> Display
The indicator shows each requested ticker as different colored candlesticks plotted on top of the main chart.
Each ticker has an associated label in front of the current bar, each component of this label can be toggled on or off to allow only the desired information to be displayed.
To retain relevance, at the start of each session, a "Session Break" line is drawn, as well as the opening price for the session. These can also be toggled.
Note: The opening price is the opening price for ALL tickers, when a ticker crosses the open on the main chart, it is crossing its own opening price as well.
> Examples
In the chart below, we can see NYSE:MCD NASDAQ:WEN and NASDAQ:JACK overlaid on a NASDAQ:SBUX chart.
From this, we can see NASDAQ:JACK was the top gainer on the day. While this was the case, it also fell roughly 4% from its peak near lunchtime. Unlike the top gainer, we can see the other 3 tickers ended their day near their daily high.
In the explanations above, the daily timeframe is used since it is the default; however, the analysis is not constrained to only days. The anchoring period can be set to any timeframe period.
In the chart below, you can observe the Daily, Weekly, and Monthly anchored charts side-by-side.
This can be used on all tickers, timeframes, and markets. While a typical application may be comparing relevant assets... the script is not limited.
Below we have a chart tracking COMEX:GCV2026 , FX:EURUSD , and COINBASE:DOGEUSD on the AMEX:SPY chart.
While these tickers are not typically compared side-by-side, here it is simply a display of the capabilities of the script.
Enjoy!
Trade Box Position Calculator (Pro-V2)Trade Box Position Calculator (Pro) is a visual risk management and position sizing tool that overlays a clean trade box directly on your chart. Instead of typing numbers in a calculator, you simply click on the chart to set your Entry, Target, and Stop levels, and the script builds a full trade map around them.
You define your Wallet Value and Risk % per trade, then the script automatically calculates:
Position size (Qty) based on distance from Entry to Stop
Total trade value
Monetary risk per trade
Potential profit and Risk:Reward ratio
Live open P&L as price moves
The trade is visualized as:
A green profit box between Entry and Target
A red risk box between Entry and Stop
Colored horizontal lines for Entry (blue), Target (green), and Stop (red)
Professional labels show detailed info:
Center label: P&L, Qty, Trade value, Risk $, R:R
Target label: distance to target, % move, potential profit $
Stop label: distance to stop, % risk, risk $
All label colors, text sizes, background transparency, and box width/offset can be customized from the settings, so you can adapt the tool to any chart style or theme.
The script also includes smart alerts that automatically follow your levels:
Entry Hit – when price touches your entry zone
Target Hit – when price touches your target
Stop Hit (3 closes) – when price has closed beyond your stop level for 3 consecutive bars (to avoid being faked out by a single wick)
You can either create separate alerts from the alertcondition() entries, or enable the “Use single combined alert()” option and set one alert with “Any alert() function call” so a single alert setup covers Entry, Target, and Stop events.
This tool is designed to help discretionary traders quickly plan trades, visualize risk vs reward on the chart, and keep position sizing consistent with their risk management rules.
Relative Strength Matrix [PUCHON]📊 Relative Strength Matrix
The Relative Strength Matrix provides a comprehensive view of how the current asset performs against a basket of other financial instruments (such as Indices, Commodities, or Currencies). By comparing price changes over two distinct timeframes (Short-Term and Long-Term), traders can quickly identify whether the asset is showing relative strength or weakness compared to the broader market or specific sectors.
✨ Features:
- 🌍 Multi-Asset Comparison: Monitor relative performance against up to 7 customizable symbols simultaneously.
- ⏳ Dual Timeframe: Analyze trends using both Short-Term (default 20) and Long-Term (default 60) lookback periods.
- 🎨 Visual Heatmap: Displays relative strength with intuitive colors:
- 🟢 Green (+): Stronger (outperforming)
- 🔴 Red (-): Weaker (underperforming)
- ⚪ Gray: Neutral
- ⚙️ Fully Customizable: Adjust symbols, colors, table position, and text size to fit your trading setup.
🧮 Calculation Logic:
The core of this indicator is the rsCalc function. It normalizes the price changes of both the base asset (current chart) and the comparison asset over a specific length, then calculates the ratio.
rsCalc(series float base, series float comp, int len) =>
nb = base / base // Normalized Base Asset Price
dc = comp / comp // Normalized Comparison Asset Price
na(nb) or na(dc) ? na : nb / dc - 1 // Relative Performance Ratio
💡 Interpretation:
- 📈 Positive Value (> 0): The current asset has appreciated more (or depreciated less) than the comparison asset. This signifies Relative Strength .
- 📉 Negative Value (< 0): The current asset has appreciated less (or depreciated more) than the comparison asset. This signifies Relative Weakness .
- ⚖️ Zero (0): Both assets have performed equally over the period.
